Seaborn 한번에 제대로 배우기

Seaborn 특징

• 여러 변수 간의 관계를 검사하기 위한 데이터 집합 지향 API
• 범주형 변수를 사용하여 관측치 또는 집계 통계량을 표시하기 위한 전문적인 지원
• 일변량 또는 이변량 분포를 시각화하고 데이터의 부분 집합 간 비교하기 위한 옵션
• 서로 다른 종류의 종속 변수에 대한 선형 회귀 모형의 자동 추정 및 표시
• 복잡한 데이터셋의 전체 구조에 대한 편리한 보기
• 복잡한 시각화를 쉽게 구축할 수 있는 다중 플롯 그리드 구조를 위한 높은 수준의 추상화
• 여러 테마가 내장된 matplotlib 그림 스타일링 제어
• 데이터의 패턴을 충실히 나타내는 색상 팔레트 선택 도구

import matplotlib as mpt
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style = 'whitegrid')

import numpy as np
import pandas as pd  
from scipy import stats

산점도(Scatter Plot)

penguins = sns.load_dataset('penguins')
penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female

penguins.shape

(344, 7)

sns.relplot(x = 'bill_length_mm', y = 'bill_depth_mm', 
            hue = 'species', # 색상
            col = 'island', # 범주별로 열을 다르게
            style = 'sex', # 모양
            size = 'flipper_length_mm', # 크기
            data = penguins);

sns.relplot(x = 'bill_length_mm', y = 'bill_depth_mm', 
            hue = 'species', size = 'body_mass_g', 
            sizes = (10, 300), # 크기의 범위
            data = penguins);

라인 플롯(Line Plot)

flights = sns.load_dataset("flights")
flights.head()

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121

sns.relplot(x = "year", y = 'passengers', kind = 'line', # 라인 플롯 
            data = flights);

dots = sns.load_dataset("dots")
dots.head()

	align	choice	time	coherence	firing_rate
0	dots	T1	-80	0.0	33.189967
1	dots	T1	-80	3.2	31.691726
2	dots	T1	-80	6.4	34.279840
3	dots	T1	-80	12.8	32.631874
4	dots	T1	-80	25.6	35.060487

sns.relplot(x = "time", y = 'firing_rate', kind = 'line', ci = None, # 신뢰구간 미표시 
            data = dots);

sns.relplot(x = "time", y = 'firing_rate', kind = 'line', estimator = None, 
            data = dots);

sns.relplot(x = "time", y = 'firing_rate', kind = 'line',
            hue = 'choice', # choice별로 색을 다르게
            style = 'choice', # choice별로 모양을 다르게 
            dashes = False, 
            markers = True, # 구간별로 점 표시
            data = dots);

sns.relplot(x = "time", y = 'firing_rate', kind = 'line',
            hue = 'align', col = 'choice',
            data = dots);

sns.relplot(x = "time", y = 'firing_rate', kind = 'line',
            style = 'choice', hue = 'coherence',
            data = dots.query("align == 'sacc'")); # 지정한 데이터에 대해서만 시각화

sns.relplot(x = "time", y = 'firing_rate', kind = 'line',
            style = 'choice', hue = 'coherence', col = 'choice',
            linewidth = 3, # 선 두께
            data = dots.query("align == 'sacc'")); # 지정한 데이터에 대해서만 시각화

tdf = pd.DataFrame(np.random.randn(40, 4),
                   index = pd.date_range('2020-01-01', periods = 40),
                   columns = ['A', 'B', 'C', 'D'])
tdf.head()

	A	B	C	D
2020-01-01	-1.129512	-0.113001	-1.409317	-0.582793
2020-01-02	0.241900	1.956451	1.200407	1.513431
2020-01-03	0.442798	-0.230053	1.220156	2.294767
2020-01-04	-0.675250	-1.555867	-0.868407	0.484697
2020-01-05	0.747861	0.827613	-0.996200	-1.266071

g = sns.relplot(kind = 'line', data = tdf)
g.fig.autofmt_xdate()

범주형 데이터(Categorical Data)

범주형 산점도(Categorical scatterplots)

• stripplot() (with kind="strip"; the default)
• swarmplot() (with kind="swarm")

penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female

sns.catplot(x = 'species', y = 'body_mass_g', data = penguins);

sns.catplot(x = 'species', y = 'body_mass_g', 
            jitter = False, 
            data = penguins);

sns.catplot(x = 'species', y = 'body_mass_g', 
            kind = 'swarm', # 겹치지 않게 펼침
            data = penguins);

np.unique(penguins.species)

array(['Adelie', 'Chinstrap', 'Gentoo'], dtype=object)

sns.catplot(x = 'species', y = 'body_mass_g', 
            kind = 'swarm', # 겹치지 않게 펼침
            hue = 'sex', 
            col = 'island', aspect = .7, # 열간격
            order = ["Gentoo", "Adelie", "Chinstrap"], # 정렬 순서 변경
            data = penguins);

범주형 분포도(Categorical distribution plots):

• boxplot() (with kind="box")
• boxenplot() (with kind="boxen")
• violinplot() (with kind="violin")

박스 플롯(Box plots)

sns.catplot(x = 'species', y = 'body_mass_g', hue = 'sex',
            kind = 'box', data = penguins);

sns.catplot(x = 'species', y = 'body_mass_g', hue = 'sex', 
            dodge = False, # boxplot을 겹치게 
            kind = 'box', data = penguins);

iris = sns.load_dataset("iris")
iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

sns.catplot(kind = "box", orient = 'h', # 축 전환
            data = iris);

박슨 플롯(Boxen plots) : 분포 모양도 표현

diamonds = sns.load_dataset('diamonds')
diamonds.head()

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

sns.catplot(x = 'cut', y = 'price', data = diamonds.sort_values("cut"),
            kind = 'boxen');

바이올린 플롯(Violin plots)

• violinplot: 커널 밀도 추정과 상자 도표 결합

sns.catplot(x = 'species', y = 'body_mass_g', data = penguins,
            kind = 'violin', hue = 'sex');

sns.catplot(x = 'species', y = 'body_mass_g', data = penguins,
            kind = 'violin', hue = 'sex',
            bw = 0.15, cut = 0);

sns.catplot(x = 'species', y = 'body_mass_g', data = penguins,
            kind = 'violin', hue = 'sex', 
            inner = 'stick', split = True);

g = sns.catplot(x = 'species', y = 'body_mass_g', data = penguins,
                kind = 'violin', inner = None);
sns.swarmplot(x = 'species', y = 'body_mass_g',
              color = 'k', size = 3,
              data = penguins, ax = g.ax);

범주형 추정치 도표(Categorical estimate plots)

• barplot() (with kind="bar")
• pointplot() (with kind="point")
• countplot() (with kind="count")

막대 플롯(Bar plots)

mpg = sns.load_dataset('mpg')
mpg.head()

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	usa	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	usa	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	usa	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	usa	ford torino

sns.catplot(x = 'origin', y = 'mpg', data = mpg,
            edgecolor = '.6', # 경계선
            hue = 'cylinders', kind = 'bar');

포인트 플롯(Point plots)

• 축의 높이를 사용하여 추정값을 인코딩하여 점 추정값과 신뢰 구간 표시

titanic = sns.load_dataset("titanic")
titanic.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

sns.catplot(x = 'who', y = 'survived', data = titanic,
            pallete = ['r','k','g'],
            markers = ['o', '^', 'H'],
            linestyles = ["-", "--", ":"],
            hue = 'class', kind = 'point');

카운트 플롯(Count plots)

sns.countplot(y = 'deck', data = titanic);

분포 시각화(Distribution Visualization)

일변량 분포(Univariate distributions)

x = np.random.randn(200) 
sns.distplot(x);

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

히스토그램(Histograms)

sns.distplot(x, bins = 20, kde = False, rug = True);

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2103: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.
  warnings.warn(msg, FutureWarning)

커널 밀도 추정(Kernel density estimation)

sns.distplot(x, hist = False, rug = True);

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2103: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.
  warnings.warn(msg, FutureWarning)

sns.kdeplot(x, shade = True);

sns.kdeplot(x) 
sns.kdeplot(x, bw = .2, label = 'bw: 0.2')
sns.kdeplot(x, bw = 1, label = "bw: 1") 
plt.legend();

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1699: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.2 for `bw_method`, but please see the docs for the new parameters and update your code.
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1699: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 1 for `bw_method`, but please see the docs for the new parameters and update your code.
  warnings.warn(msg, FutureWarning)

sns.kdeplot(x, shade = True, cut = 0)
sns.rugplot(x);

x = np.random.gamma(10, size = 500) 
sns.distplot(x, kde = False, fit = stats.gamma);

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

이변량 분포(Bivariate distributions)

산점도(Scatterplots)

• jointplot: 두 개의 변수 간의 이변량(또는 joint) 관계와 별도의 축에 각각의 일변량(또는 marginal) 분포가 모두 표시되는 다중 패널 플롯 생성
• 산점도 + 분포도

mean = [0, 1]  
cov = [(1, .3), (.3, 1)]
data = np.random.multivariate_normal(mean, cov, 200) 
df = pd.DataFrame(data, columns = ["x", "y"])

sns.jointplot(x = 'x', y = 'y', data = df);

육각 빈 플롯(Hexbin plots)

x, y = np.random.multivariate_normal(mean, cov, 200).T 
with sns.axes_style("white"):
  sns.jointplot(x = x, y = y, kind = 'hex')

커널 밀도 추정(Kernel density estimation)

sns.jointplot(x = 'x', y = 'y', data = df, kind = 'kde');

sns.kdeplot(df.x, df.y) 
sns.kdeplot(df.x, color = 'r') 
sns.kdeplot(df.y, color = 'g', vertical = True);

/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1689: FutureWarning: The `vertical` parameter is deprecated and will be removed in a future version. Assign the data to the `y` variable instead.
  warnings.warn(msg, FutureWarning)

sns.kdeplot(df.x, df.y) 
sns.rugplot(df.x, color = 'r') 
sns.rugplot(df.y, color = 'g', vertical = True);

/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2112: FutureWarning: Using `vertical=True` to control the orientation of the plot  is deprecated. Instead, assign the data directly to `y`. 
  warnings.warn(msg, FutureWarning)

cmap = sns.cubehelix_palette(as_cmap = True, dark = 0, light = 1, reverse = True) # 어두울수록 0, 밝을수록 1 
sns.kdeplot(df.x, df.y, cmap = cmap, n_levels = 60, shade = True);

/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

g = sns.jointplot(x = 'x', y = 'y', data = df, kind = 'kde')
g.plot_joint(plt.scatter, s = 30, linewidth = 1, marker = ".")
g.ax_joint.collections[0].set_alpha(0)

페어와이즈 관계 시각화(Visualizing pairwise relationships) : 상관관계 시각화

sns.pairplot(penguins);

sns.pairplot(penguins, hue = 'species');

<seaborn.axisgrid.PairGrid at 0x7f48573427d0>

g = sns.PairGrid(penguins) 
g.map_diag(sns.kdeplot)
g.map_offdiag(sns.kdeplot, n_levels = 6);

히트맵(Heat Map)

udata = np.random.rand(20, 30) 
sns.heatmap(udata);

ndata = np.random.randn(20, 30) 
sns.heatmap(ndata, center = 0);

flights_p = flights.pivot('month', 'year', 'passengers') 
sns.heatmap(flights_p);

sns.heatmap(flights_p, annot = True, fmt = 'd'); # 숫자 표시

sns.heatmap(flights_p, cmap = "BuPu", # 히트맵 색상 변경
            cbar = False); # 컬러바 삭제

grid_kws = {'height_ratios': {.9, 0.01}, "hspace": .5}
f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw = grid_kws) 
ax = sns.heatmap(flights_p, ax = ax, 
                 cbar_ax = cbar_ax,
                 cbar_kws = {'orientation' : 'horizontal'})

클러스터맵(Cluster Map)

brain_networks = sns.load_dataset("brain_networks", header = [0, 1, 2], index_col = 0) 
brain_networks.head()

network	1		2		3		4		5		...	16			17
node	1		1		1		1		1		...	3	4		1		2		3		4
hemi	lh	rh	lh	rh	lh	rh	lh	rh	lh	rh	...	rh	lh	rh	lh	rh	lh	rh	lh	rh	lh
0	56.055744	92.031036	3.391576	38.659683	26.203819	-49.715569	47.461037	26.746613	-35.898861	-1.889181	...	0.607904	-70.270546	77.365776	-21.734550	1.028253	7.791784	68.903725	-10.520872	120.490463	-39.686432
1	55.547253	43.690075	-65.495987	-13.974523	-28.274963	-39.050129	-1.210660	-19.012897	19.568010	15.902983	...	57.495071	-76.393219	127.261360	-13.035799	46.381824	-15.752450	31.000332	-39.607521	24.764011	-36.771008
2	60.997768	63.438793	-51.108582	-13.561346	-18.842947	-1.214659	-65.575806	-85.777428	19.247454	37.209419	...	28.317369	9.063977	45.493263	26.033442	34.212200	1.326110	-22.580757	12.985169	-75.027451	6.434262
3	18.514868	12.657158	-34.576603	-32.665958	-7.420454	17.119448	-41.800869	-58.610184	32.896915	11.199619	...	71.439629	65.842979	-10.697547	55.297466	4.255006	-2.420144	12.098393	-15.819172	-37.361431	-4.650954
4	-2.527392	-63.104668	-13.814151	-15.837989	-45.216927	3.483550	-62.613335	-49.076508	18.396759	3.219077	...	95.597565	50.960453	-23.197300	43.067562	52.219875	28.232882	-11.719750	5.453649	5.169828	87.809135

5 rows × 62 columns

networks = brain_networks.columns.get_level_values("network")
used_networks = np.arange(1, 10) 
used_columns = (networks.astype(int).isin(used_networks)) 
brain_networks = brain_networks.loc[:, used_columns] 

network_pal = sns.husl_palette(17, s = .5)
network_lut = dict(zip(map(str, used_networks), network_pal)) 
network_colors = pd.Series(networks, index = brain_networks.columns).map(network_lut)  

sns.clustermap(brain_networks.corr(), center = 0, cmap = 'RdBu_r',
               row_colors = network_colors, col_colors = network_colors, 
               linewidth = .5, figsize = (12, 12));

선형 관계 시각화(Visualizing linear relationships)

선형 회귀 모델 시각화 함수

sns.regplot(x = 'flipper_length_mm', y = 'body_mass_g', data = penguins);

sns.lmplot(x = 'flipper_length_mm', y = 'body_mass_g', data = penguins);

sns.lmplot(x = 'flipper_length_mm', y = 'body_mass_g', data = penguins,
           col = 'sex', hue = 'island');

sns.lmplot(x = 'flipper_length_mm', y = 'body_mass_g', data = penguins, hue = 'species', 
           x_estimator = np.mean); # 평균값들을 표시

sns.lmplot(x = 'flipper_length_mm', y = 'body_mass_g', data = penguins,
           row = 'sex', col = 'island', hue = 'species');

다른 종류의 모델

penguins = sns.load_dataset("penguins")
penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female

penguins["long_bill"] = (penguins.bill_length_mm > penguins['bill_length_mm'].mean()) 
penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	long_bill
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male	False
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female	False
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female	False
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	False
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female	False

sns.lmplot(x = 'body_mass_g', y = 'long_bill', data = penguins, y_jitter = 0.03,
           logistic = True); # 로지스틱 회귀 곡선

/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm

sns.lmplot(x = 'bill_length_mm', y = 'flipper_length_mm',
           lowess = True, data = penguins);

다른 상황의 회귀

sns.jointplot(x = 'body_mass_g', y = 'flipper_length_mm', 
              kind = 'reg', data = penguins);

sns.pairplot(penguins,
             x_vars = ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm'],
             y_vars = ['body_mass_g'], hue = 'species',
             height = 4, aspect = .8,
             kind = 'reg');

구조화된 다중 플롯 그리드

FacetGrid

sns.set(style = 'ticks')

g = sns.FacetGrid(penguins, col = 'sex')

g = sns.FacetGrid(penguins, col = 'sex', hue = 'species')  
g.map(plt.hist, 'body_mass_g');
plt.legend();

g = sns.FacetGrid(penguins, col = 'species', hue = 'sex') 
g.map(sns.regplot, 'bill_length_mm', 'bill_depth_mm');
g.add_legend();

페어와이즈 데이터 관계(pairwise data relationships)

g = sns.PairGrid(penguins) 
g.map(plt.scatter);

g = sns.PairGrid(penguins) 
g.map_diag(plt.hist) # 대각부분만 히스토그램
g.map_offdiag(plt.scatter); # 대각이외의 부분은 산점도

그림 미학 제어

Seaborn 스타일

def randplot(flip = 1):
  for i in range(1, 7):
    plt.plot(np.random.randn(50).cumsum()); 

randplot();

sns.set()
randplot();

sns.set_style('whitegrid')
randplot();

스타일 요소 재정의

f = plt.figure(figsize = (6, 6))
gs = f.add_gridspec(2, 2) 

with sns.axes_style('darkgrid') :
  ax = f.add_subplot(gs[0, 0])
  randplot()

with sns.axes_style('white') :
  ax = f.add_subplot(gs[0, 1])
  randplot()

with sns.axes_style('ticks') :
  ax = f.add_subplot(gs[1, 0])
  randplot()

with sns.axes_style('whitegrid') :
  ax = f.add_subplot(gs[1, 1])
  randplot() 

f.tight_layout()

sns.axes_style()

{'axes.axisbelow': True,
 'axes.edgecolor': '.8',
 'axes.facecolor': 'white',
 'axes.grid': True,
 'axes.labelcolor': '.15',
 'axes.spines.bottom': True,
 'axes.spines.left': True,
 'axes.spines.right': True,
 'axes.spines.top': True,
 'figure.facecolor': 'white',
 'font.family': ['sans-serif'],
 'font.sans-serif': ['Arial',
  'DejaVu Sans',
  'Liberation Sans',
  'Bitstream Vera Sans',
  'sans-serif'],
 'grid.color': '.8',
 'grid.linestyle': '-',
 'image.cmap': 'rocket',
 'lines.solid_capstyle': 'round',
 'patch.edgecolor': 'w',
 'patch.force_edgecolor': True,
 'text.color': '.15',
 'xtick.bottom': False,
 'xtick.color': '.15',
 'xtick.direction': 'out',
 'xtick.top': False,
 'ytick.color': '.15',
 'ytick.direction': 'out',
 'ytick.left': False,
 'ytick.right': False}

스케일링 플롯 요소

sns.set() 

sns.set_context("paper")
randplot();

sns.set_context("talk")
randplot();

sns.set_context("poster")
randplot();

sns.set_context("notebook", font_scale = 1.5, rc = {"lines.linewidth": 2.5})
randplot();

컬러 팔레트 선택

sns.set()

질적 색상 팔레트

current_palette = sns.color_palette()
sns.palplot(current_palette)

xkcd 색상 측량에서 정의된 색상 사용

• xkcd 색상표: https://xkcd.com/color/rgb/

plt.plot(np.random.randn(50).cumsum(), sns.xkcd_rgb["pale red"], lw = 3)
plt.plot(np.random.randn(50).cumsum(), sns.xkcd_rgb["medium green"], lw = 3)
plt.plot(np.random.randn(50).cumsum(), sns.xkcd_rgb["denim blue"], lw = 3)

[<matplotlib.lines.Line2D at 0x7f48514aeb10>]

colors = ["windows blue", "amber", "faded green", "dusty purple"]
sns.palplot(sns.xkcd_palette(colors))

순차 색상 팔레트

sns.palplot(sns.color_palette("Blues"))
sns.palplot(sns.color_palette("BuGn_r"))
sns.palplot(sns.color_palette("GnBu_d"))

순차적 입방체 팔레트

sns.palplot(sns.color_palette("cubehelix", 8))

sns.palplot(sns.cubehelix_palette(8))

sns.palplot(sns.cubehelix_palette(8, start = .5, rot = .75))

sns.palplot(sns.cubehelix_palette(8, start = 2, rot = 0, dark = 0, light = 0.95, reverse = True))